where \(\sigma \)  and \(D_{e}\)  denote the downsampling factor and dimension of feature maps; \(\Theta _{f}\)  indicates the learnable parameters in \(\phi \) . Additionally, \(\phi \)  usually adopts a top-down fusion strategy. That is, it gradually fuses feature maps from deep semantic representations to shallow local cues.However, \(\phi \)  in most previous methods has obvious differences (More details can be seen in Section  of Appendix), which can result in different performances.To avoid the influence of different \(\phi \) , we set the frequently-used fusion strategy like those in [1]}, [2]}, where \(\sigma \)  is equal to 1.
